LSTM-Char-Large
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Harvard University,New York University (NYU)
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 26 August 2015
- Authors
- Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 19M
- Training data
- 929,000 tokens
- Epochs
- 25
19M (Table 3)
25 epochs based on code (https://github.com/yoonkim/lstm-char-cnn/blob/master/main.lua) and "We train for 25 epochs on non-Arabic and 30 epochs on Arabic data (which was sufficient for convergence)"
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 2.7 × 10¹⁵ FLOP
- How it was established
- Operation counting
6 FLOP / parameter / token * 19000000 parameters * 929000 tokens * 25 epochs = 2.64765e+15 FLOP
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
code, MIT license: https://github.com/yoonkim/lstm-char-cnn
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 2,033
- Benchmark data
- LSTM-Char-Large
Sources
Where this record came from and when it was last checked.
- Reference
- Character-Aware Neural Language Models
- Last updated
- 28 November 2025
What the numbers mean
What this model is
LSTM-Char-Large was published by Harvard University,New York University (NYU), in United States of America, in August 2015. The organisation is categorised as academia,Academia.
It works in Language, and is recorded as doing language modeling.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
The training run consumed about 2.7 × 10¹⁵ FLOP. That figure describes the cost of creating it and has no bearing on how quickly it generates text.
It was trained on about 929,000 tokens of text.
Answers
LSTM-Char-Large — common questions
How much compute was used to train LSTM-Char-Large?
Around 2.7 × 10¹⁵ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run LSTM-Char-Large?
None. LSTM-Char-Large is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is LSTM-Char-Large open source?
No. LSTM-Char-Large has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does LSTM-Char-Large have?
LSTM-Char-Large has 19M parameters. 19M (Table 3). That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created LSTM-Char-Large?
LSTM-Char-Large was published by Harvard University,New York University (NYU), based in United States of America, categorised as academia,Academia.
When was LSTM-Char-Large released?
LSTM-Char-Large was published in August 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is LSTM-Char-Large used for?
LSTM-Char-Large works in Language, and is recorded as handling language modeling. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.